Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing

Abstract

Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue. A particular example is the planar Fabry-Perot (FP) scanner, which yields high-resolution images but takes several minutes to sequentially map the photoacoustic field on the sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: First, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP scanner and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in-vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction methods that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of PAT scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.

submitted to "Physics in Medicine and Biology"

References in corpus (2)

• On the Adjoint Operator in Photoacoustic Tomography
• Non-Equispaced Grid Sampling in Photoacoustics with a Non-Uniform FFT

Cited by in corpus (19)

• Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
• Model based learning for accelerated, limited-view 3D photoacoustic tomography
• Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning
• A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation
• Infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in PDE
• Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
• A New Sparsification and Reconstruction Strategy for Compressed Sensing Photoacoustic Tomography
• A Framework for Directional and Higher-Order Reconstruction in Photoacoustic Tomography
• Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation
• A continuous adjoint for photo-acoustic tomography of the brain
• Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
• Quantitative Photoacoustic Imaging of Two-photon Absorption
• Fully three-dimensional sound speed-corrected multi-wavelength photoacoustic breast tomography
• Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography
• Compressed sensing photoacoustic tomography reduces to compressed sensing for undersampled Fourier measurements
• All-optical photoacoustic tomography via beam deflection
• Well-posedness for Photoacoustic Tomography with Fabry-Perot Sensors
• Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization
• Performance Bounds for LASSO under Multiplicative Noise: Applications to Pooled RT-PCR Testing